A Comparison Study About Parameter Optimization Using Swarm Algorithms

Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to adjust the models’ parameters automatically. These models were built around two machine learning-based algorithms, an extreme learning machine neural network, and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets in different contexts indicated that optimized algorithms models perform better in convergence, precision, and robustness than the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool for analyzing different data contexts. Thus, this study shows that the optimized algorithm models (in particular the ELM PSO model) are more accurate than all experimental evaluations.